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The Price Effect of Building Energy Ratings in the Dublin ... Stanley.pdf · The Price Effect of...
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The Price Effect of Building Energy Ratings in the Dublin Residential Market
Ronan C. Lyons, Department of Economics, Trinity College Dublin; Spatial Economics Research
Centre, London School of Economics
Sean Lyons, Economic and Social Research Institute, Dublin; Department of Economics, Trinity College
Dublin
Sarah Stanley, Economic and Social Research Institute, Dublin; Sustainable Energy Authority of Ireland
ABSTRACT
This paper is an empirical study on the relationship between the energy performance rating of residential
homes in the Dublin market between 2009 and 2014 and their market price, controlling for building
type, size, age, and location. Initial results suggest that energy efficiency has a significant, positive
relationship with list price. A 50-point improvement in the Energy Performance Indicator (kWh/m2/yr)
is associated with a 1.5% higher list price. Alternatively, using the Building Energy Rating metric, a
one-point improvement in the 15-point scale from G to A1 yields a list price increase of 1%. This
mirrors findings for efficiency price premiums on a nationwide basis from Hyland et al. (2013). We also
find that it is important to include controls for the age of the dwelling to avoid biased energy efficient
estimates in the hedonic model.
Key words: domestic building energy ratings, hedonic valuation, Ireland
2014 International Energy Policy & Programme Evaluation Conference, Berlin
1. Introduction
In an era of environmental awareness and concerted action toward sustainable energy management,
energy efficiency is a key challenge. Buildings account for 40% of energy consumption in Europe (EC
2010) and residential homes contribute to around one sixth of emissions globally (IEA 2013).
Promisingly, small improvements to the structure of buildings (such as greater insulation or replacing
inefficient heating systems) can have a significant impact on their energy performance. Despite the
environmental benefits and energy savings that can be gained from more energy efficient homes,
evidence of buyer appreciation of the value of energy efficiency and domestic investment in efficiency
measures to boost property values has been limited. Supplementary literature has begun to emerge on
willingness to pay for energy efficiency (Banfi et al. 2008; Brounen & Kok 2011; Cajias & Piazolo
2013; Cerin et al. 2014; DECC 2013; EC 2013a; Hyland et al. 2013; McLean et al. 2013; Popescu et al.
2012), whether energy savings are considered in buying decisions (Amecke 2012; Murphy 2014), and if
financing should stem from private or public sources (Allcot & Greenstone 2012; EC 2013b;
Gilllingham et al. 2009, 2012). This paper adds to this literature, providing hedonic value estimates for
residential energy efficiency in the Dublin region of Ireland over the past five years. We use a variety of
categorical and continuous variables to represent energy efficiency of dwellings and find a broadly
consistent pattern of effects whereby better efficiency attracts a higher valuation. We also find that it is
important to include controls for the age of the dwelling, because omitting this characteristic can lead to
biased estimates of the energy efficiency effect.
1.1. Policy Background
Since the signing of the Kyoto Protocol in 1997 world leaders have committed to internationally
binding emission reduction targets, the latest of which aims to reduce emissions by at least 18%
compared to 1990 levels by 2020 (UNFCCC 2012). In the European Union, in conjunction with carbon
and renewable policies, the energy efficiency directive aims for a 20% reduction in energy demand by
2020. One of the main policy tools for reducing energy consumption and advancing awareness of the
energy performance of buildings are Energy Performance Certificates (EPCs). EPCs provide a
benchmark for ranking buildings based on their energy consumption per square metre and their
associated CO2 emissions. Since the 1990s, a combination of voluntary and mandatory EPC schemes
across residential, commercial and/or public buildings have emerged in countries such as Australia
(NatHERS, Green Star), Russia (Energy Passport), the United States (LEED, Energy Star, HERS Index),
Japan (CASBEE), and Singapore (Energy Smart, BCA Green Mark). In the European Union, the 2002
EU Directive on the Energy Performance of Buildings (EPBD, recast 2010) introduced a mandatory
requirement for member states to provide specific information on a building’s energy performance and
recommendations for energy saving measures to prospective purchasers and tenants in property
transactions. Despite a possible fine of up to €5,000 issued for non-compliance, rates of compliance
across Europe prior to the recast Directive, or certainly the advertising of Energy Performance
Certificates during a sale or rental, have been relatively low. From 2013 onwards, amongst other things,
the recast EPBD ensures that all properties advertised for sale or for rental, excluding certain buildings
such as protected structures, include an official energy certificate (The European Parliament the Council
of the EU 2010).
2014 International Energy Policy & Programme Evaluation Conference, Berlin
2. Previous Research
The majority of empirical evidence on the impact of energy certificates on building prices supports
the assumption that people are willing to pay extra for more efficient properties in both the commercial
and residential sector. Studies in the commercial sector, primarily in the United States, suggest that
green offices acquire a price premium from 13% to 30% (Chegut et al. 2011; Eichholtz et al. 2010,
2013; Fuerst & McAllister 2011a, 2011b; Miller et al. 2008; Pivo & Fisher 2010; Wiley et al. 2008;).
Similarly positive results of a lower magnitude are observed in the residential sector. Figure 1 displays
price premiums between 2% and 10% in A, B and C-rated homes and price discounts in lower rated
homes when compared to D-rated properties in the Netherlands and in Ireland (Brounen & Kok 2011;
Hyland et al. 2013).
Figure 1: Price premiums relative to D-rated residential properties in Ireland and the Netherlands
A wider study undertaken by the European Commission (2013) compares residential premiums
associated with EPCs in five European countries: Austria, Belgium, France, Ireland, and the UK. The
sales price effects from increased energy efficiency are significant and positive in all cases apart from
Oxford in the UK, although this may be due to age being an omitted variable in the UK dataset (Figure
2).
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Figure 2: Residential price effect of one-letter or equivalent improvement in EPC rating (EC, 2013a)
Further studies in Europe, Australia, the United States, and Singapore confirm findings that
residential buyers are willing to pay a premium for energy efficient housing (Australian Bureau of
Statistics 2008; Cajias & Piazolo 2013; Cerin et al. 2014; DECC 2013; Deng et al. 2013; Dinan &
Miranowski 1989; Fuerst et al. 2013; Kahn & Kok 2012), although some papers have produced more
mixed results (Amecke 2012; Murphy 2014; Walls et al. 2013; Yoshida & Sugiura 2010).
3. Data
Information on 2,780 residential properties for sale in the Dublin region from January 2009 to June
2014 is used in this analysis, sourced from daft.ie, the largest real estate website in Ireland capturing
90% of all properties advertised on the market.1 The daft.ie data contains information on the sales price,
location (split by 25 postcodes in Dublin), time period of advertisement, housing type (apartment,
detached, terraced, etc.), size (in square metres), and age. For the purposes of this study, the data was
limited to properties between the prices of €30,000 and €2 million, 25m2 and 400 m
2, have three floors
or less, and contain up to six bedrooms. List prices are used as opposed to the price at point of sale due
to data constraints. However, in the absence of actual sales prices, previous analysis show that appraised
values are a reliable proxy for transaction prices (Malpezzi 2003; Shimizu et al. 2012) and that the
variation over time in asking prices and transaction prices in Ireland are highly correlated (Lyons 2013).
In Ireland, following the EU Directive on the Energy Performance of Buildings being passed into
Irish law in 2006, Building Energy Ratings (BERs) were compulsory for any dwellings seeking planning
permission, and since January 2009, for existing residences for sale or for rent. Equivalent to an energy
performance certificate, the Irish BER is an objective measurement of the energy use for space and hot
water heating, ventilation and lighting based on standard occupancy. Ratings range from G to A1 on a
15-point scale, where A-rated properties are the most energy efficient and will tend to have the lowest
energy bills (see Appendix 1 for sample Irish BER Certificate). Assessments are completed by BER
1 http://www.daft.ie/about/ [Accessed 12 August 2014]
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Assessors registered with the Sustainable Energy Authority of Ireland (SEAI), the issuing authority for
BER Certificates in Ireland. Certificates are accompanied by an Advisory Report which identifies the
possible ways to improve the energy performance of a building. Once a BER is carried out, the
Certificate is valid for 10 years, provided that no significant change is made to the building in that time
(SEAI 2014a). In August 2014, there were 475,495 Domestic BERs on the Public Register (27% of total
private households in the country); 62% fell between a C1 and D2-rating, 13% were either A or B-rated,
and 25% were E-rated or lower (SEAI 2014b). Almost a third of total BERs on the register (137,206)
were issued for the Dublin area.
Figure 3: Domestic BERs on the Public Register (SEAI, 2014b)
The data in this study differs from previous analysis undertaken in Ireland by Hyland et al.
(2013) in several important ways. Rather than nationwide, this analysis looks in closer detail at the Irish
capital city, Dublin, split between 25 districts. Data specific to smaller geographic areas enables a better
approximation of neighbourhood effects and reduces the risk of heterogeneity at a county level,
particularly prevalent in urban areas. The age of the building is included, removing the risk of omitted
variable bias in Hyland et al. (2013). Age of domestic buildings may be correlated with other
characteristics such as size, dwelling type, and indeed BER ranking. Given the implied correlation
between BER rating and the age of a dwelling, the positive price effects associated with vintage housing
may conceal negative impacts of poor energy performance. In addition, square metres provide a more
accurate indicator of size than the number of bedrooms since room sizes can vary significantly both
within and across buildings in the same category. Finally, three different measures of energy efficiency
are used: the Energy Perfomance Indicator (EPI), a continuous BER variable with a 15-point BER scale
from A1 to G, and BER dummy variables indicating the premiums associated with each BER ranking
compared to a C1-rating. The EPI is included on all BER Certificates as way of measuring energy
performance relative to scale for a given year, calculated in the residential sector by dividing the total
energy consumption by total floor area and/or the occupation rate (SEAI 2014):
EPIr = kWh/m2/yr [1]
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Of the 2,870 dwellings observed, 7% have an A or B-rating, 58% fall under a C or D category,
and 35% have a BER of E or lower. Compared to the national public register, the properties for sale in
the Dublin area between 2009 and 2012 have a higher percentage of buildings in the lower rated
categories (35% compared to 25%). Figure 4 displays the distribution of the total BERs in Dublin
according to the SEAI database, compared to the sample of houses for sale with a BER. Figure 5 shows
the distribution of properties in our sample by building type compared to all properties in the Dublin
population (with and without BERs). Semi-detached housing is the most common type of dwelling in
Dublin. However, apartments, terraced and detached housing are better represented in our sample due to
more BERs associated with these categories on the market.
Figure 4: Distribution of BERs in Dublin
Figure 5: Distribution of Property Type in Dublin
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4. Methodology
To estimate the value that residential users place on domestic energy efficiency, we use a technique
known as hedonic regression, frequently applied in real estate and environmental analysis. This involves
breaking down a property’s value into each of its constituent parts; for example, location, building type,
age, and size. Other factors which affect housing prices such as the liquidity of the market, availability
of supply, and population changes are assumed to be uncorrelated with building characteristics and
therefore should not affect the statistical relationship between housing characteristics and price.
However, depending on the purposes of the study, these factors are sometimes captured by a time
variable. Early applications of hedonic valuation provided a method of predicting a price before it is
known, for example, using a car’s horsepower, design, etc. to appraise its worth (Court 1939). In the
housing market, this is particularly useful when estimating pre sale property values or for the purposes
of securing a loan. Later, hedonic methods were applied to quantify the value of individual attributes to a
property’s overall worth when the price is known (Nourse 1967; Ridker & Henning 1967). The implicit
value of the traits of a property are identified by regressing its revealed price (at point of sale or list
price) on the attributes of a dwelling, statistically represented as the following:
P = a0 + a1X1 + a2X2 + a3X3 + ... + anXn + e [2]
Where P refers to the price of the dwelling, the Xi’s represent the various characteristics of a
property (age, size, etc.), ai indicates each of the characteristic’s implied value, and e is a random error
term. While equation [2] assumes a linear function, other functional forms may be employed by
transforming the relevant variables. For example if property value is not linear in the number of
bedrooms, a squared term may be added to capture the diminishing marginal effects of size. As well as
the attributes of a property (x), the location (n) and the energy rating (c) are also included, such that:
P = f(x,n,c) + e [3]
In addition to structural differences between buildings, a spatial or location variable enables the
inclusion of a fixed effect associated with each neighbourhood that influence purchasing decisions. By
including all the traits assumed to influence a property’s price it is possible to estimate people’s
willingness to pay for individual features when its characteristics change. At its most basic level, given
two similar properties in the same locality with different energy ratings, the difference in price indicates
people’s willingness to pay for increased energy efficiency.
2014 International Energy Policy & Programme Evaluation Conference, Berlin
5. Results
Table 1 presents the results for the hedonic price model relating the sales price of residences in
Dublin (n=2780) to their efficiency rating, building type, age, location, and time period of
advertisement. Energy efficiency is measured firstly by an Energy Performance Indicator (EPI) in model
1 and 2, a continuous BER scale in model 3, and a categorical variable in model 4.
Table 1: Hedonic Model Results
Dependent variable:
Price
Model 1:
Coefficient
Std.
error
Model 2:
Coefficient
Std.
error
Model 3:
Coefficient
Std.
error
Model 4:
Coefficient
Std.
error
Efficiency Indicators:
EPI -0.0003*** 0.000
EPI (log) -0.086*** 0.002
BER (continuous) 0.010*** 0.003
BER label ranking:
G -0.131*** 0.034
F -0.051 0.031
E2 -0.077** 0.032
E1 -0.037 0.029
D2 -0.051** 0.026
D1 -0.016 0.026
C3 -0.019 0.026
C2 -0.041 0.027
C1 REF REF REF REF
B3 0.017 0.032
B2 -0.028 0.047
B1 -0.294** 0.140
A3 0.025 0.163
House type:
Apartment -0.178*** 0.046 -0.180*** 0.046 -0.174*** 0.045 -0.173*** 0.044
Apt. (Top) 0.027 0.025 0.027 0.025 0.022 0.025 0.023 0.025
Apt. (Ground) 0.027 0.026 0.028 0.026 0.024 0.026 0.027 0.026
Detached 0.210*** 0.054 0.209*** 0.054 0.207*** 0.053 0.209*** 0.054
Terrace -0.185*** 0.048 -0.185*** 0.048 -0.182*** 0.047 -0.181*** 0.047
Semi-Detached -0.125*** 0.032 -0.126*** 0.032 -0.125*** 0.032 -0.120*** 0.031
Size:
Square Metres 0.805*** 0.207 0.808*** 0.208 0.817*** 0.210 0.812*** 0.209
Age of building:
Pre-1900 0.221*** 0.057 0.210*** 0.054 0.195*** 0.050 0.208*** 0.054
1900s 0.203*** 0.052 0.191*** 0.049 0.176*** 0.045 0.188*** 0.048
1910s 0.169*** 0.043 0.160*** 0.049 0.147*** 0.048 0.160*** 0.049
1920s 0.231*** 0.059 0.227*** 0.058 0.220*** 0.056 0.219*** 0.056
1930s 0.196*** 0.050 0.192*** 0.049 0.183*** 0.047 0.190*** 0.049
1940s 0.148*** 0.038 0.142*** 0.037 0.134*** 0.034 0.143*** 0.037
1950s 0.055 0.028 0.049* 0.028 0.039 0.028 0.049 0.029
1960s -0.025 0.024 -0.027 0.024 -0.034 0.024 -0.035 0.025
1970s 0.072*** 0.019 0.073*** 0.019 0.069*** 0.018 0.068*** 0.021
1980s 0.097*** 0.025 0.098*** 0.025 0.097*** 0.025 0.097*** 0.025
1990s REF REF REF REF
2000s -0.043** 0.019 -0.051*** 0.020 -0.047** 0.019 -0.039* 0.021
Constant 8.826 9.216 8.638 8.753
R2
0.773 0.772 0.772 0.773
N 2780 2780 2780 2780
Significant at ***1%, **5%, *10% levels respectively.
2014 International Energy Policy & Programme Evaluation Conference, Berlin
The results show that energy efficiency has a significant, positive relationship with sales prices in
Dublin’s residential market. In the first instance, a 50-point improvement in the EPI is associated with a
1.5% higher list price. This matches findings from the European Commission (2013a). In the Flanders
area of Belgium, a 50-point improvement in the CPEB metric (the Belgian equivalent of the Irish EPI) is
associated with a 2.15% increase in sales price of homes and, narrowing the analysis to the capital city,
Brussels, a 50-point CPEB improvement led to a 1.45% increase in price.
In the second model, we have transformed the EPI functional form from linear to logs to provide
an interpretation of the relationship between EPI and price in terms of percentage rather than a unit
change in EPI. The coefficient indicates that a 10% increase in the EPI is associated with a 0.86%
increase in Dublin property list prices, ceteris paribus. In terms of the BER, including a continuous 15-
point BER scale in model 3 yields a list price increase of 1% for every one-point improvement in the
BER scale. This is consistent with Hyland et al. (2013) where each rating decline was associated with a
reduction in price of 1.3%. For the most part, model 4 produces statistically insignificant results,
probably indicating that our sample is too small to segment this variable so finely. However, the signs
roughly conform to expectations.
To test whether the value premium associated with residential efficiency is sensitive to the
economic cycle, additional models were also run which included annual EPI interaction terms in model
1 and annual BER interaction terms in model 3. The inclusion of yearly interaction terms for the EPI and
the BER test the hypothesis that the relationship between energy efficiency and price is dependent upon
the year of advertisement. However, the results for the interaction terms were statistically insignificant
suggesting that energy efficiency is not any more (or less) prized now than in 2009 or 2010 when the
economy was in a deep recession.
The regression results also show that detached homes sell at a price premium, size has a
significant, positive impact on price, and older buildings, particularly those built in the 1920s and pre-
1900 have the highest price premiums compared to newer builds from the 1990s. Location variables are
excluded from the results table but are used to control for the effect of locality on housing price and
displayed the expected price effects depending on the desirability of different areas.
A further insight from the analysis is that it is important to include controls for the age of the
dwelling when modelling the value of residential energy efficiency. Omitting age effects leads to a
downward bias in the estimated BER effects. Figure 6 charts the coefficient for the decade a house was
built. Compared to Model 1, a model with no treatment of energy efficiency shows smaller vintage
premiums, indicative of correlation between the EPI and age, with the bias increasing the earlier the
vintage. We can deduce, therefore, that in previous analysis lower BER properties were also likely to be
of an older vintage and, according to the results, more valuable, introducing a downward bias to our
coefficient. Thus, in older properties with poor BER ratings, the discounting effect may have been
underestimated.
2014 International Energy Policy & Programme Evaluation Conference, Berlin
Figure 6: Age of property coefficient, with and without an efficiency explanatory variable
One shortcoming of the data available to us was that not all properties in our dataset included a BER
rating when advertising their property. We do not observe the BERs of those who did not advertise it.
If there are unobserved characteristics (e.g. some aspect of dwelling quality) affecting both the decision
whether to advertise the BER and the value of the dwelling, this selection bias could distort the
estimated values for efficiency found by our models. Hyland et al. (2013) use a Heckman selection
model to account for this possibility but ultimately find no correlation between the error terms in the
selection and outcome equations (i.e. the decision to advertise BER was not correlated with unobserved
factors influencing price) and obtained similar results using both the OLS and Heckman approach. We
have not identified a suitable exclusion restriction to enable us to try this approach using the dataset in
this paper, but it is worth considering for the future.
6. Conclusions
The results from our model show that energy efficiency has a positive and significant effect on
residential property in the Dublin market. An increase by one-letter in the BER is associated with 1%
higher list prices, holding all other variables constant. This is consistent with the national estimate for
Ireland in Hyland et al. (2013), which was based on less detailed location data and excluded the age of
the building. Modelling efficiency on a continuous scale using the Energy Performance Indicator shows
a 1.5% higher list price with every 50-point improvement in the EPI, producing estimates that are
consistent with a related study in Brussels (EC, 2013a). These results suggest that there is a willingness
to pay for greater efficiency when buying a home. Since investing in energy efficiency can involve a
significant upfront financial cost, initial investment may be offset if the value of efficiency is considered
by potential buyers and captured in the selling price. Further research could include a comparison of the
price premiums derived here, and a range of potential savings and investment scenarios associated with
increasing a building’s EPI or BER.
While this paper focuses on Dublin, these are initial results from an updated dataset covering the
whole of Ireland; future research may expand the study to all counties.
Acknowledgements
We are grateful for funding from the Sustainable Energy Authority of Ireland and the ESRI Energy
Policy Research Centre. The usual disclaimer applies.
2014 International Energy Policy & Programme Evaluation Conference, Berlin
References
Amecke, H., 2012. “The impact of energy performance certificates: a survey of German homeowners.”
Energy Policy 46: 4–14.
Allcot, H. and M. Greenstone. 2012. “Is there an Energy Efficiency Gap?” Journal of Economic
Perspectives, 26 (1): 3-28
Australian Bureau of Statistics. 2008. Energy Efficiency Rating and House Price in the ACT.
Department of the Environment, Water, Heritage and the Arts, Canberra.
Banfi, S., Farsi, M., Filippini, M. and M. Jakob. 2008. “Willingness to pay for energy saving measures
in residential buildings.” Energy Econ. 30 (2): 503–516.
Brounen, D. and N. Kok. 2011. “On the economics of energy labels in the housing market.” J. Environ.
Econ. Manag. 62 (2): 166–179.
Cajias,M. and D. Piazolo. 2013. “Green performs better: Energy efficiency and financial return on
buildings.” Journal of Corporate Real Estate. 15(1): 53-72.
Cerin, P., Hassel, L. and N. Semenova. 2014. “Energy Performance and Housing Prices.” Sustainable
Development Working Paper
Chegut, A., Eichholtz, P. and N. Kok. 2011. The value of green buildings: new evidence from the United
Kingdom. Paper presented at the International AREUEA Meeting, Rotterdam, Netherlands
Court, A. 1939. “Hedonic price indexes with automotive examples.” In: The Dynamics of Automobile
Demand. General Motors Company, New York.
Deng, Y., Li, Z. and J.M. Quigley. 2011. “Economic Returns to Energy-Efficiency Investments in the
Housing Market; Evidence from Singapore.” Regional Science and Urban Economics, 42 (3): 506-515
Department of Energy and Climate Change (UK). 2013. An Investigation of the Effect of EPC Ratings
on House Prices.
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/207196/20130613_-
_Hedonic_Pricing_study_-_DECC_template__2_.pdf
Dinan, T.M. and J.A. Miranowski. 1989. “Estimating the implicit price of energy efficiency
improvements in the residential housing market: a hedonic approach.” J. Urban Econ. 25 (1): 52–67.
Eichholtz, P., Kok, N. and J.M. Quigley. 2010. “Doing well by doing good? Green office buildings.”
Am. Econ. Rev. 100 (5): 2492–2509.
Eichholtz, P., Kok, N. and J.M. Quigley. 2013. “The economics of green building.” Rev. Econ. Stat. 95
(1): 50–63.
2014 International Energy Policy & Programme Evaluation Conference, Berlin
European Commission (EC). 2010. DIRECTIVE 2010/31/EU OF THE EUROPEAN PARLIAMENT
AND OF THE COUNCIL (Recast). http://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32010L0031&from=EN
European Commission (EC). 2013a. “Energy performance certificates in buildings and their impact on
transaction prices and rents in selected EU countries.” EC, Brussels.
http://ec.europa.eu/energy/efficiency/buildings/doc/20130619-
energy_performance_certificates_in_buildings.pdf
European Commission (EC). 2013b. “Financial support for energy efficiency in buildings.” EC,
Brussels.http://ec.europa.eu/energy/efficiency/buildings/doc/report_financing_ee_buildings_com_2013_
225_en.pdf
Fuerst, F. and P. McAllister. 2011a. “The impact of Energy Performance Certificates on the rental and
capital values of commercial property assets.” Energy Policy 39 (10): 6608–6614.
Fuerst, F. and P. McAllister, P. 2011b. “Green noise or green value? Measuring the effects of
environmental certification on office values.” Real Estate Econ. 39 (1): 45–69.
Gillingham, K., Newell, R. and K. Palmer. 2009. “Energy Efficiency Economics and Policy.” Annual
Review of Resource Economics, Annual Reviews 1(1): 597-620.
Gillingham, K., Harding, M. and D. Rapson. 2012. “Split Incentives in Residential Energy
Consumption” Energy Journal 33 (2): 37-62.
Hyland, M., Lyons, R.C. and S. Lyons. 2013. “The value of domestic building energy efficiency –
evidence from Ireland.” Energy Economics 40: 943–952.
International Energy Agency (IEA). 2013. “CO2 Emissions from Fuel Combustion – Highlights.”
http://www.iea.org/publications/freepublications/publication/co2emissionsfromfuelcombustionhighlight
s2013.pdf
Kahn, M.E. and N. Kok. 2012. “The value of green labels in the Californian housing market.”
http://www.usgbc.org/sites/default/files/ValueofGreenHomeLabelsStudy_July2012.pdf
Lyons, R.C. 2013. “Price signals and bid-ask spreads in an illiquid market: the case of residential
property in Ireland, 2006–2011.” http://ssrn.com/abstract=2205742
Malpezzi, S. 2003. “Hedonic pricing models: a selective and applied review.” In: O'Sullivan, Tony,
Gibb, Kenneth (Eds.), Housing Economics and Public Policy: Essays in honour of Duncan Maclennan,
2003. Blackwell.
McLean, A., Horvath, A. and H. Janos Kiss. 2013. “How does an increase in energy efficiency affect
housing prices? A Case Study of a Renovation.” In: Book of proceedings: 20th Annual Conference of the
European Real Estate Society. ÖKK-Verlag, Vienna, pp. 39-55. ISBN 978-3-85437-323-0
2014 International Energy Policy & Programme Evaluation Conference, Berlin
Miller, N., Spivey, J. and A. Florance. 2008. “Does Green Pay Off?” Journal of Real Estate Portfolio
Management 14 (4)
Murphy, L. 2014. “The Influence of the Energy Performance Certificate: The Dutch Case.” Energy
Policy 67: 664-672
Nourse, H. O. 1967. “The Effect of Air Pollution on House Values.” Land Economics, 43: 181-189
Pivo, G. and J.D. Fisher. 2010. “Income, Value, and Returns in Socially Responsible Office Properties.”
Journal of Real Estate Research 32 (3): 243-270.
Popescu, D., Bienert, S., Schutzenhofer, C. and R. Boazu. 2012. “Impact of Energy Efficiency Measures
on the Economic Value of Buildings.” Applied Energy 89: 454-463.
Ridker, R. and J. Henning. 1967. “The determinants of residential property values with special reference
to air pollution.” The Review of Economics and Statistics 49: 246-257.
Shimizu, C., Nishimura, K.G. and T. Watanabe1. 2012. “House prices from magazines, realtors, and the
Land Registry”. In: Bank for International Settlements (Ed.), Property markets and financial stability:
29–38.
Sustainable Energy Authority of Ireland. 2014. “BER information leaflet”
http://www.seai.ie/Your_Building/BER/Your_Guide_to_Building_Energy_Rating.pdf
Sustainable Energy Authority of Ireland. 2014. “BER Statistics – Domestic BERs Dashboard.”
http://www.seai.ie/Your_Building/BER/BER_FAQ/FAQ_BER/General/Domestic-BERs-Dashboard.pdf
The European Parliament the Council of the European Union, 2010. Directive 2010/31/EU on the
energy performance of buildings (recast). Off. J. Eur. Union 53, 153 (Legislation).
United Nations Framework Convention on Climate Change (UNFCCC), 2012. “Kyoto Protocol.”
http://unfccc.int/kyoto_protocol/items/2830.php
Walls, M., Palmer, K. and T. Gerarden. 2013. “Is Energy Efficiency Capitalized into Home Prices?
Evidence from Three US Cities.” Resources for the Future Discussion Paper.
http://www.rff.org/RFF/Documents/RFF-DP-13-18.pdf
Wiley, J.A., Benefield, J.D. and K.H. Johnson. 2008. “Green design and the market for commercial
office space.” J. Real Estate Financ. Econ. 41: 228–243.
Yoshida, J. and A. Sugiura. 2010. “Which “Greenness” is valued? Evidence from Green Condominiums
in Tokyo.” Munich Personal RePEc Archive. http://mpra.ub.uni-
muenchen.de/23124/1/MPRA_paper_23124.pdf
2014 International Energy Policy & Programme Evaluation Conference, Berlin